Update app.py

app.py

@@ -1,22 +1,21 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the pipeline once
-pipe = pipeline("image-classification", model="google/vit-base-patch16-224")
+# Wybierz lżejszy model
+model_name = "facebook/deit-tiny-patch16-224"
+
+pipe = pipeline("image-classification", model=model_name)
 
-# Define function for Gradio
 def classify_image(img):
     results = pipe(img)
     return {res["label"]: float(res["score"]) for res in results}
 
-# Gradio interface
 demo = gr.Interface(
     fn=classify_image,
-    inputs=gr.Image(type="filepath"),   # user uploads/selects image
-    outputs=gr.Label(num_top_classes=5), # show top 5 predictions with scores
-    title="Image Classification with ViT",
-    description="Upload an image (e.g., SHrek.png) and see predicted labels."
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.Label(num_top_classes=5),
+    title="Lekka klasyfikacja obrazów",
+    description=f"Model: {model_name}"
 )
 
-# Launch
 demo.launch()